Knowledge from Probability

TL;DR

This paper provides a probabilistic framework for understanding inductive knowledge and belief, predicting that only highly probable propositions are believed and challenging some existing belief-revision principles.

Contribution

It introduces a novel probabilistic analysis of inductive knowledge using relations of normality, linking belief to probability and critiquing traditional belief-revision principles.

Findings

Highly probable propositions are believed

Many belief-revision principles fail under the model

The analysis predicts beliefs about the future and natural laws

Abstract

We give a probabilistic analysis of inductive knowledge and belief and explore its predictions concerning knowledge about the future, about laws of nature, and about the values of inexactly measured quantities. The analysis combines a theory of knowledge and belief formulated in terms of relations of comparative normality with a probabilistic reduction of those relations. It predicts that only highly probable propositions are believed, and that many widely held principles of belief-revision fail.